Abstract
Human brain functional connectivity (FC) networks, estimated based on resting-state functional magnetic resonance imaging (rs-fMRI), has become a promising tool for imaging-based brain disease diagnosis. Conventional low-order FC network (LON) usually characterizes pairwise temporal correlation of rs-fMRI signals between any pair of brain regions. Meanwhile, high-order FC network (HON) has provided an alternative brain network modeling strategy, characterizing more complex interactions among low-order FC sub-networks that involve multiple brain regions. However, both LON and HON are usually constructed within a fixed and relatively wide frequency band, which may fail in capturing (sensitive) frequency-specific FC changes caused by pathological attacks. To address this issue, we propose a novel “multi-frequency HON construction” method. Specifically, we construct not only multiple frequency-specific HONs (intra-spectrum HONs), but also a series of cross-frequency interaction-based HONs (inter-spectrum HONs) based on the low-order FC sub-networks constructed at different frequency bands. Both types of these HONs, together with the frequency-specific LONs, are used for the complex network analysis-based feature extraction, followed by sparse regression-based feature selection and the classification between mild cognitive impairment (MCI) patients and normal aging subjects using a support vector machine. Compared with the previous methods, our proposed method achieves the best diagnosis accuracy in early diagnosis of Alzheimer’s disease.
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References
Gauthier, S., Reisberg, B., Zaudig, M., Petersen, R.C., Ritchie, K., Broich, K., Belleville, S., Brodaty, H., Bennett, D., Chertkow, H., Cummings, J.L.: Mild cognitive impairment. Lancet 367(9518), 1262–1270 (2006)
Zhu, X., Suk, H.I., Lee, S.W., Shen, D.: Subspace regularized sparse multitask learning for multiclass neurodegenerative disease identification. IEEE Trans. Biomed. Eng. 63(3), 607–618 (2016)
Zhu, X., Suk, H.I., Wang, L., Lee, S.W., Shen, D.: A novel relational regularization feature selection method for joint regression and classification in AD diagnosis. Med. Image Anal. 38, 205–214 (2017)
Allen, E.A., Damaraju, E., Plis, S.M., Erhardt, E.B., Eichele, T., Calhoun, V.D.: Tracking whole-brain connectivity dynamics in the resting state. Cereb. Cortex 24, 663–676 (2012)
Chen, X., Zhang, H., Lee, S.-W., Shen, D.: Hierarchical high-order functional connectivity networks and selective feature fusion for MCI classification. Neuroinformatics 1–14 (2017)
Wang, J., Wang, Q., Peng, J., Nie, D., Zhao, F., Kim, M., Zhang, H., Wee, C.Y., Wang, S., Shen, D.: Multi-task diagnosis for autism spectrum disorders using multi-modality features: a multi-center study. Hum. Brain Mapp. 38(6), 3081–3097 (2017)
Zhang, H., Chen, X., Shi, F., Li, G., Kim, M., Giannakopoulos, P., Haller, S., Shen, D.: Topographical information-based high-order functional connectivity and its application in abnormality detection for mild cognitive impairment. J. Alzheimers Dis. 54(3), 1095–1112 (2016)
Zhang, Y., Zhang, H., Chen, X., Lee, S.-W., Shen, D.: Hybrid high-order functional connectivity networks using resting-state functional MRI for mild cognitive impairment diagnosis. Scientific Reports (2017)
Salvador, R., Martinez, A., Pomarol-Clotet, E., Gomar, J., Vila, F., Sarro, S., Capdevila, A., Bullmore, E.: A simple view of the brain through a frequency-specific functional connectivity measure. NeuroImage 39(1), 279–289 (2008)
Tewarie, P., Hillebrand, A., van Dijk, B.W., Stam, C.J., O’Neill, G.C., Van Mieghem, P., Meier, J.M., Woolrich, M.W., Morris, P.G., Brookes, M.J.: Integrating cross-frequency and within band functional networks in resting-state MEG: a multi-layer network approach. NeuroImage 142, 324–336 (2016)
Wee, C.Y., Yap, P.T., Denny, K., Browndyke, J.N., Potter, G.G., Welsh-Bohmer, K.A., Wang, L., Shen, D.: Resting-state multi-spectrum functional connectivity networks for identification of MCI patients. PLoS ONE 7(5), e37828 (2012)
Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: uses and interpretations. NeuroImage 52(3), 1059–1069 (2010)
Chen, X., Zhang, H., Gao, Y., Wee, C.Y., Li, G., Shen, D.: High-order resting-state functional connectivity network for MCI classification. Hum. Brain Mapp. 37(9), 3282–3296 (2016)
Zhang, Y., Zhou, G., Jin, J., Zhao, Q., Wang, X., Cichocki, A.: Aggregation of sparse linear discriminant analysis for event-related potential classification in brain-computer interface. Int. J. Neural Syst. 24(1), 1450003 (2014)
Zhang, Y., Zhou, G., Jin, J., Zhao, Q., Wang, X., Cichocki, A.: Sparse Bayesian classification of EEG for brain-computer interface. IEEE Trans. Neural Netw. Learn. Syst. 27(11), 2256–2267 (2016)
Zhang, Y., Wang, Y., Jin, J., Wang, X.: Sparse Bayesian learning for obtaining sparsity of EEG frequency bands based feature vectors in motor imagery classification. Int. J. Neural Syst. 27(2), 1650032 (2017)
Acknowledgements
This work is partially supported by NIH grants (EB006733, EB008374, EB009634, MH107815, AG041721, and AG042599).
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Zhang, Y., Zhang, H., Chen, X., Shen, D. (2017). Constructing Multi-frequency High-Order Functional Connectivity Network for Diagnosis of Mild Cognitive Impairment. In: Wu, G., Laurienti, P., Bonilha, L., Munsell, B. (eds) Connectomics in NeuroImaging. CNI 2017. Lecture Notes in Computer Science(), vol 10511. Springer, Cham. https://doi.org/10.1007/978-3-319-67159-8_2
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DOI: https://doi.org/10.1007/978-3-319-67159-8_2
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